Intelligent automated assistant systems that perform functions in response to user requests are common in many contexts. Such systems may be used, for example, in navigation systems and customer service applications. Building a fully automated, intelligent conversational assistant system is nearly impossible using existing methods. Such systems have, however, improved significantly over the last decade due to automated learning methods.
Automated learning methods typically require some kind of feedback (or manually annotated training data) in order for the behavior of the system to evolve. For instance, existing speech recognition and language understanding systems rely on static training data, from which models are trained. Periodically, the static training data is augmented, and the models are either retrained or adapted. New training data can be chosen randomly or using more sophisticated techniques such as active learning, which tries to obtain the most informative samples.
Representative human/machine interaction training data is often obtained from feedback through human correction. When the system makes an error, a human can detect the error and provide the system with a correct response. If this feedback is provided in real time, and the user sees only the corrected output, the configuration of the system is referred to as a “Wizard of Oz” (WOZ) configuration.